ATOMS Project 2002-2003: Assumptions, Projects & Discoveries v1.3
ATOMS Project 2002-2003: Assumptions, Projects & Discoveries (PDF, 0.49MB)
Roger O. Smith, PhD, OT & Kathy Longenecker Rust, MS, OTR
As the ATOMS Project activities progress, we have been watching our preliminary and interim findings. To summarize the soft and empirical findings, we created two documents. The first document relates to the ATOMS Project assumptions, combining where we were as our work commenced and the assumptions that we have identified during our investigations. The second document summarizes the ATOMS Project discoveries as they link to some of our specific research activities. They are presented here consecutively.
The charge from NIDRR provides the backdrop for the assumptions and discoveries. NIDRR suggested that the research a) perform a needs assessment pertaining to outcomes measurement in AT, b) explore available and new outcome measures and strategies for AT outcomes, and c) perform abandonment investigations related to the previous activities.
Assumptions
- Assistive Technology (AT) devices serve as one intervention for people with disabilities within a set of many interventions they typically receive.
- In a natural environment, AT use is often used concurrently with a variety of other interventions and services
- Devices and services are two different components of AT interventions.
- There also exists a group of AT users that are not part of a service system. Collecting outcome data for this group would be particularly challenging. They are, however, stakeholders of AT outcomes.
- Despite a federal law mandating the consideration of AT, little evidence suggests that all students with disabilities have access to AT.
- AT devices and services cross service delivery systems, including the vocational rehabilitation, the educational, the medical and the independent living systems.
- The context in which the device is used and the AT services obtained are covariates that can confound and even reverse the outcomes of AT interventions.
- A variety of outcome dimensions contribute domains to the overall outcome. These include self-satisfaction of products and services, costs, participation in activities, task performance, goal achievement, AT device use, and quality of life.
- By convention and by definition, AT device use can result in a negative outcome.
- There are many measurement and research methodologies that are not typically used for AT outcomes (e.g., goal attainment scaling, dynamic norming subjective elicitation of data, MAU and Bayes) that we need to understand for their potential contribution to an outcomes system.
ATOMS Project Activities & Discoveries 2002-2003 |
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Projects |
Discoveries |
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Field Scans |
AT Instrument Update and Review |
While dozens of assistive technology (AT) measurement instruments exist, few have been devised with outcomes in mind. Most have been created as part of the process to identify and select devices to match a need to an individual AT consumer. |
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Treatment of AT in Current/Emerging Health & Rehabilitation Outcome Measures |
Health & rehabilitation functional performance & related outcome measures rarely include AT as a co-variate. Many treat AT as an impairment that lowers performance scores, and even fewer instruments isolate the impact of AT in the outcome score (Rust & Smith, 2004a, 2004b). |
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Outcome Measures Used in AT Research & Development |
Product developers of AT devices report substantial interest in AT outcomes, measurements and potential use of valid outcome measurement instruments (Rust & Smith, 2004b, 2003a). |
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Next Generation Data Collection Technology |
Handheld computers provide a dynamic and efficient mechanism for collecting large & individualized amounts of outcomes-related data. The newer hardware & software components available open many doors for naturalistic data collection (Kennedy, 2003). |
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As AT outcomes & data will likely require a multi-dimensional representation of data, new multi-dimensional data displays must be considered as a part of AT outcomes instrumentation. |
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Use Multi-attribute (MAU) and Bayes Approaches in Outcomes Data Collection |
Decision analysis data collection & models, such as Bayesian estimation & multi-attribute utility techniques are heavily used in related fields. These may provide new strategies for measuring key components of AT outcomes. |
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Review of Taxonomies of AT Outcomes Instruments |
Considering traditional measurement theory & methods, the task/activity may provide the best conceptual vehicle for efficiently measuring AT outcomes. |
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Legal Implications of AT Outcomes Instruments |
Legal issues surrounding AT outcomes data collection & application are significant & more integral to AT outcomes instrument development than initially considered. Further attention must be targeted on the ethical & legal implication of AT outcomes (Mendelsohn, Schwanke, & Smith, 2004). |
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History of AT Outcomes |
AT outcomes research & outcomes measurement research are relatively new areas of inquiry. Interest has only been documented over the past 20 years or so (Smith, Rust, Lauer, & Boodey, 2004). |
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Methods to Identify AT Device Use |
AT outcomes research to date has not identified a method to identify the frequency and intensity of AT device use (Whyte, Smith, Fennema-Jansen, & Edyburn, 2003). |
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Implications of Qualitative Research Methods and Qualitative Data on AT Outcomes |
Qualitative data provides a depth of information in technology acquisition and may have important applications within a future AT outcome measurement system (Harris, 2004). |
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Comparison of Cost Outcome Methods |
Where cost analysis methods are maturing in health care, applications and strategic methods that specifically address AT are still in early conceptual development (Harris & Sprigle, 2003; Sprigle & Harris, 2004). |
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Negative Aspects of AT |
The use of multi-focal/bifocal eyeglasses and walkers may have a negative impact on gait speed & quality, suggesting that AT may be a significant contributor to falls (Joerger, 2003). |
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Satisfaction with AT |
"Satisfaction" should never be used without a qualifier. In the field of AT there can be satisfaction with the device, satisfaction with the service, or satisfaction with performance. Only the latter appears to be outcome. The first two appear to be outcome-precursor variables (Rust & Smith, 2004c). |
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Focus Groups |
Service Program Administrators Focus Group |
Service program directors identify 10 primary areas of outcomes consistent with previous literature. |
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Consumer Focus Groups |
Consumers of AT devices hold a unique perspective on what AT outcomes mean. AT "outcomes" depict terminology and a concept created by the service delivery and funding stakeholders (Taugher, 2003, 2004). |
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Town Halls |
Legal Issues Town Hall |
This topic specific town hall identified 27 issues that all met a high priority when ranked. The discussion was not able to generate consensus on priorities. Issues were specific to service delivery models. |
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AT Outcomes Town Halls |
Participants voiced the universal need for better AT outcomes measurement instruments and reporting systems regardless of AT service perspective. |
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Database Projects |
Service Delivery System |
Service and delivery program records and data contain little to no outcomes data. Service delivery records & data remain widely variable & inconsistent from program to program (Schwanke & Smith, in press; Schwanke & Smith, 2004a). |
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Vocational Rehabilitation |
The Rehabilitation Services Administration database (named 911) exists as one of the largest disability-related databases that contains relevant AT device & service information for outcomes analysis. This database might provide a foundation for examining AT outcomes in the vocational rehabilitation sector (Schwanke & Smith, 2004b). |
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NHIS |
The NHIS (National Health Information Survey-Disability) provides a large database that might serve as a basis for AT outcomes analysis. However, the NHIS database contains numerous problems in its design, reducing the potential usefulness of the database to measure AT outcomes (Moser, 2004a; Moser, 2004b). |
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Ohio Schools |
Public school systems have a significant need for tracking AT outcomes. A web-based centralized system seems to be a feasible data collection medium (Fennema-Jansen, 2004a, 2004b; Wilson, Smith, Fennema-Jansen, & Edyburn, 2003). |
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References
Fennema-Jansen, S. A. (2004a). Technical report-The assistive technology infusion project (ATIP) database (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Fennema-Jansen, S. A. (2004b). Measuring AT outcomes using the student performance profile: Analysis and recommendation. RESNA 27th International Conference on Technology & Disability: Research, Design, Practice & Policy.
Harris, F. (2004). Technical report-Qualitative research discussions, April 29, 2003 (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Harris, F., & Sprigle, S. (2003). Cost analyses in assistive technology research. Assistive Technology, 15(1), 16-27.
Joerger, T. F. (2003). Risk of falling: The relationship between assistive technology use and the quality and speed of gait. Unpublished master's thesis, University of Wisconsin, Milwaukee.
Kennedy, B. L. (2003). Ecological electronic diary for outcomes measurement. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.
Mendelsohn, S. B., Schwanke, T. D., & Smith, R. O. (2004). Overview of legal issues in assistive technology outcomes measurement. RESNA 27th International Conference on Technology & Disability: Research, Design, Practice & Policy.
Moser, C. S. (2004a). The 1995 and 1995 NHIS Phase II Disability Followback Survey-Child Questionnarie: A critical analysis of the data relating to AT and its implications for future AT survey research. Unpublished doctoral dissertation, University of Wisconsin-Milwaukee.
Moser, C. S. (2004b). Technical report-Data base analysis: 1994 and 1995 NHIS Phase II Disability Followback Survey, Child Questionnaire (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004a). Technical report - The inclusion of assistive technology outcomes in current health and rehabilitation outcome measures (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004b). Technical report-Outcome measures used in AT research and development (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Rust, K. L., & Smith, R. O. (2004c). Satisfaction with assistive technology: What are we measuring? RESNA 27th International Conference on Technology & Disability: Research, Design, Practice & Policy.
Rust, K. L., & Smith, R. O. (2003a). Outcome data needs for assistive technology research and development. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.
Rust, K. L., & Smith, R. O. (2003b). Treatment of Assistive Technology Interventions in Health and Rehabilitation Outcome Assessments. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.
Schwanke, T. D., & Smith, R. O. (in press). Assistive technology outcomes in work settings, WORK: A Journal of Prevention, Assessment &Rehabilitation.
Schwanke, T. D., & Smith, R. O. (2004a). Technical report-Service programs database (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Schwanke, T. D., & Smith, R. O. (2004b). Technical report-Vocational rehabilitation database analysis: RSA-911 case service report and database linking (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Smith, R. O., Rust, K. L., Lauer, A., & Boodey, E. (2004). Technical report-History of assistive technology outcomes (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Sprigle, S., & Harris, F. (2004). Technical report-Comparison of cost outcome methods (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Taugher, M. (2003). Available information and outcomes of assistive technology: A consumer perspective focus group. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.
Taugher, M. (2004). Focus groups on assistive technology use and outcomes: A consumer perspective (1.0). University of Wisconsin-Milwaukee. Retrieved, from the World Wide Web: http://www.uwm.edu/CHS/atoms/activities/.
Whyte, F. L., Smith, R. O., Fennema-Jansen, S. A., & Edyburn, D. L. (2003). Assistive Technology Device Use Inventory: The need and development of a conceptual model. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.
Wilson, S., Smith, R. O., Fennema-Jansen, S. A., & Edyburn, D. L. (2003). Launching a large scale assistive technology service delivery and outcome tracking system in the Public schools. RESNA 26th International Conference on Technology and Disability: Research, Design, Practice and Policy.